pacman::p_load(readxl, gifski, gapminder,
plotly, gganimate, tidyverse)Hands-on Exercise 1
1. Getting Started
1.1 Install and launching R packages
The Code chunk below uses p_load() function of pacman package to check if tidyverse packages are installed in the computer. If they are, then they will be launched into R.
1.2 Importing the data
nfortunately, mutate_each_() was deprecated in dplyr 0.7.0. and funs() was deprecated in dplyr 0.8.0. In view of this, we will re-write the code by using mutate_at() as shown in the code chunk below.
col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls", sheet="Data") %>%
mutate_at(col, as.factor) %>%
mutate(Year = as.integer(Year))Instead of using mutate_at(), across() can be used to derive the same outputs.
col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls", sheet="Data") %>%
mutate(across(col, as.factor)) %>%
mutate(Year = as.integer(Year))2. Animated Data Visualisation: gganimate methods
gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time.
transition_*()defines how the data should be spread out and how it relates to itself across time.view_*()defines how the positional scales should change along the animation.shadow_*()defines how data from other points in time should be presented in the given point in time.enter_*()/exit_*()defines how new data should appear and how old data should disappear during the course of the animation.ease_aes()defines how different aesthetics should be eased during transitions.
2.1 Building a static population bubble plot
In the code chunk below, the basic ggplot2 functions are used to create a static bubble plot.
ggplot(globalPop, aes(x = Old, y = Young, size = Population, colour = Country)) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}', x = '% Aged', y = '% Young') 
2.2 Building the animated bubble plot
In the code chunk below,
- transition_time() of gganimate is used to create transition through distinct states in time (i.e. Year).
ease_aes()is used to control easing of aesthetics. The default is linear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.
ggplot(globalPop, aes(x = Old, y = Young, size = Population, colour = Country)) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}', x = '% Aged', y = '% Young') +
transition_time(Year) +
ease_aes('linear') 
3. Animated Data Visualisation: plotly
Both ggplotly() and plot_ly() support key frame animations through the frame argument/aesthetic. They also support an ids argument/aesthetic to ensure smooth transitions between objects with the same id (which helps facilitate object constancy).
3.1 Building an animated bubble plot: ggplotly() method
In this sub-section, we will create an animated bubble plot by using ggplotly() method.
gg <- ggplot(globalPop, aes(x = Old, y = Young,
size = Population, colour = Country, frame = Year)) +
geom_point(aes(size = Population, frame = Year),
alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged', y = '% Young')
ggplotly(gg)Notice that although show.legend = FALSE argument was used, the legend still appears on the plot. To overcome this problem, theme(legend.position='none') should be used as shown in the plot and code chunk below.
gg <- ggplot(globalPop, aes(x = Old, y = Young,
size = Population, colour = Country, frame = Year)) +
geom_point(aes(size = Population),
alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged', y = '% Young') +
theme(legend.position='none')
ggplotly(gg)3.2 Building an animated bubble plot: plot_ly() method
bp <- globalPop %>%
plot_ly(x = ~Old, y = ~Young, size = ~Population, color = ~Continent,
sizes = c(2, 100), frame = ~Year, text = ~Country,
hoverinfo = "text", type = 'scatter', mode = 'markers') %>%
layout(showlegend = FALSE)
bp